Biomedical Mathematics Group

IBS BIMAG 2026 Winter Internship Presentation
2026-02-10 13:30 - 15:00 (Asia/Seoul) | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Assigned time
Chair
Topic
Mentee
Mentor
Title

13:30–13:35
Myna Lim
Digital Health &
Clinical Methodology
Suhyeon Hwang
Myna Lim
Development of a Shortened Version of Cognitive Flexibility Inventory (CFI)

13:35–13:40
Sugwon Cho
Myna Lim
Machine Learning–Based Development of a Short-Form Scale for Subjective Perceptions of Sleep Medications

13:45–13:50
Taekeun Kim
Kangmin Lee
Revealing pattern of slow wave activity for insomnia diagnosis

13:50–13:55
Jeongmin Kim
Jin Woo Hyun
Iterative Multi-Kernel Self-Supervised Learning for Multimodal Wearable Data

13:55–14:00
Kangmin Lee
Sleep
Minjae Kim
Kangmin Lee
Improving prediction of circadian phase with mathematical model of sleep

14:00–14:05
Daewon Jeong
Yun Min Song
Analysis of Sleep Patterns Leading to Improved Sleep and Enhanced Alertness

14:05–14:10
Seunghun Lee
Yun Min Song
Analysis on relation between alertness and sleep quality during treatment

14:10–14:15
Hyeong Jun Jang
Molecular &
Cellular dynamics
Junyoung Lee
Hyeong Jun Jang
AI-based expansion of the validity condition for enzyme kinetic model

14:15–14:20
Se Jun Ahn
Hyeong Jun Jang
Practical application of 2D-3D reaction-diffusion model in transporter

14:20–14:25
Jaehun Jeong
Gyuyoung Hwang
Unifying framework for circadian temperature robustness: The roles of waveform and intercellular coupling

14:25–14:30
Jaehyuk Yang
Hyun Kim
Improving Spatial Gene Prediction via scLENS-Driven SpaGE

14:30–14:35
Math & AI
for dynamics
Gia Hyun
Dongju Lim
Weak form estimation of history-dependent epidemiological dynamics

14:35–14:40
Gyeongwan Gu
Jin Woo Hyun
Deep Predictor-Corrector Networks for Robust Parameter Estimation in Non-autonomous System with Discontinuous Inputs

14:40–14:45
Jiwon Jang
Gyuyoung Hwang
Flow matching with Physics-informed principle to solve inverse problem

14:45–14:50
Minjun Kim & Taekwan Kim
Kangmin Lee
Characterizing Circadian Modulation of Heart Rate Through Physics-Informed Neural Network Inference

Toward a Foundation Model for Molecular Tasks - Sungbin Lim
2026-02-10 16:00 - 18:00
Abstract
(국문) 최근 거대언어모델(LLM)을 기술의 발전은 AI4Science 분야에서 Foundation Model 개발에 대한 세계적인 관심을 촉발하였다. 그 중에서도 신약 및 신소재 개발에 연계된 Molecular 도메인에서의 Foundation Model 연구는 막대한 산업적 영향력과 가치를 가지고 있다. 본 발표에서는 분자 구조 생성, 물성, 및 반응 예측 문제에 적용되기 위해 필요한 Multimodal LLM 연구 성과와 방향성을 소개하고자 한다.
(English) The advancement of Large Language Models (LLMs) is drawing huge interest in developing Foundation Models for the field of AI4Science. In particular, Foundation Model research within the molecular domain, specifically linked to drug discovery and advanced materials, holds immense industrial impact and value. In this presentation, we introduce recent achievements in Multimodal LLM research for molecular structure generation, property prediction, and chemical reaction forecasting. This is a joint work with LG AI Research.
